Nonlinear Model Reduction for Uncertainty Quantification in Large-Scale Inverse Problems
We present a model reduction approach to the solution of large-scale statistical inverse problems in a Bayesian inference setting. A key to the model reduction is an efficient representation of the non-linear terms in the reduced model. To achieve this, we present a formulation that employs masked p...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
John Wiley & Sons, Inc.,
2011-03-17T12:04:42Z.
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| Subjects: | |
| Online Access: | Get fulltext |